mixmo.core.loss.SoftCrossEntropyLoss

class mixmo.core.loss.SoftCrossEntropyLoss(config_args, device, config_loss=None)

Bases: mixmo.core.loss.AbstractLoss

Soft CrossEntropy loss that specifies the proper forward function for AbstractLoss

__init__(config_args, device, config_loss=None)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(config_args, device[, config_loss])	Initializes internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Adds a child module to the current module.
apply(fn)	Applies fn recursively to every submodule (as returned by .children()) as well as self.
buffers([recurse])	Returns an iterator over module buffers.
children()	Returns an iterator over immediate children modules.
cpu()	Moves all model parameters and buffers to the CPU.
cuda([device])	Moves all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Sets the module in evaluation mode.
extra_repr()	Set the extra representation of the module
float()	Casts all floating point parameters and buffers to float datatype.
forward(input, target)	Defines the computation performed at every call.
get_accumulator_stats([format, split])	Gather tracked stats into a dictionary as formatted strings
half()	Casts all floating point parameters and buffers to half datatype.
load_state_dict(state_dict[, strict])	Copies parameters and buffers from state_dict into this module and its descendants.
modules()	Returns an iterator over all modules in the network.
named_buffers([prefix, recurse])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix])	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Returns an iterator over module parameters.
print_details()
register_backward_hook(hook)	Registers a backward hook on the module.
register_buffer(name, tensor)	Adds a persistent buffer to the module.
register_forward_hook(hook)	Registers a forward hook on the module.
register_forward_pre_hook(hook)	Registers a forward pre-hook on the module.
register_parameter(name, param)	Adds a parameter to the module.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
share_memory()
start_accumulator()
state_dict([destination, prefix, keep_vars])	Returns a dictionary containing a whole state of the module.
to(*args, **kwargs)	Moves and/or casts the parameters and buffers.
train([mode])	Sets the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
zero_grad()	Sets gradients of all model parameters to zero.

Attributes

dump_patches

This allows better BC support for load_state_dict().

_forward(input, target)

Cross entropy that accepts soft targets :param pred: predictions for neural network :param targets: targets, can be soft :param size_average: if false, sum is returned instead of mean

Examples:

input = torch.FloatTensor([[1.1, 2.8, 1.3], [1.1, 2.1, 4.8]])
input = torch.autograd.Variable(out, requires_grad=True)

target = torch.FloatTensor([[0.05, 0.9, 0.05], [0.05, 0.05, 0.9]])
target = torch.autograd.Variable(y1)
loss = cross_entropy(input, target)
loss.backward()